{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def unpickle(file):\n",
    "    import pickle\n",
    "    with open(file, 'rb') as fo:\n",
    "        dict = pickle.load(fo, encoding='latin1')\n",
    "    return dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_CIFAR10_train(ROOT):\n",
    "    X = []\n",
    "    Y = []\n",
    "    for b in range(1,6):\n",
    "        file_x = os.path.join(ROOT,\"cifar-10-batches-py/data_batch_{}\".format(b))\n",
    "        dict_x = unpickle(file_x)\n",
    "        X.append(dict_x[\"data\"])\n",
    "        Y.append(dict_x[\"labels\"])\n",
    "    Xtr = np.concatenate(X).reshape(50000, 3, 32, 32).transpose(0, 2, 3, 1).astype(\"float\")\n",
    "    Ytr = np.concatenate(Y)\n",
    "    del X, Y\n",
    "    return Xtr,Ytr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtr, Ytr = load_CIFAR10_train(\"datasets/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 32, 32, 3)\n",
      "(50000,)\n",
      "<built-in method astype of numpy.ndarray object at 0x000001C8BC170630>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(Xtr[1000].astype(\"uint8\"))\n",
    "print(Xtr.shape)\n",
    "print(Ytr.shape)\n",
    "print(Xtr.astype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "f395c2e1a992dc8a929f6f371f2b4d25c07e31a6b8b09b005bd71d5c43f24d90"
  },
  "kernelspec": {
   "display_name": "Python 3.8.8 64-bit ('pytorch': conda)",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
